Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
摘要 作者提出了一种只使用整数运算的quantization方式，比起float point运算效率更高。同时提出了一种相应的训练方式来保证quantization之后的准确率。这篇文章的方法提升了accuracy和on-device latency之间的trade off，并且可以在MobileNets上使用。 1 introduction 作者总结了目前有效将庞大的神经网络应用在资源更为有限的
相关文章
- 1. 论文阅读——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 2. 【论文阅读笔记】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 3. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- 4. Bag of Freebies for Training Object Detection Neural Networks
- 5. （转）A Recipe for Training Neural Networks
- 6. STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS
- 7. Domain-Adversarial Training of Neural Networks
- 8. DANN：Domain-Adversarial Training of Neural Networks
- 9. Weighted-Entropy-based Quantization for Deep Neural Networks
- 10. Strategies For Pre-Training Graph Neural Networks
- 更多相关文章...
- • Scala for循环 - Scala教程
- • Swift for 循环 - Swift 教程
- • RxJava操作符（七）Conditional and Boolean
- • Kotlin学习（二）基本类型
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
- 1. 在windows下的虚拟机中,安装华为电脑的deepin操作系统
- 2. 强烈推荐款下载不限速解析神器
- 3. 【区块链技术】孙宇晨：区块链技术带来金融服务的信任变革
- 4. 搜索引起的链接分析-计算网页的重要性
- 5. TiDB x 微众银行 | 耗时降低 58%，分布式架构助力实现普惠金融
- 6. 《数字孪生体技术白皮书》重磅发布（附完整版下载）
- 7. 双十一“避坑”指南：区块链电子合同为电商交易保驾护航！
- 8. 区块链产业，怎样“链”住未来？
- 9. OpenglRipper使用教程
- 10. springcloud请求一次好用一次不好用zuul Name or service not known
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 论文阅读——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 2. 【论文阅读笔记】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 3. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- 4. Bag of Freebies for Training Object Detection Neural Networks
- 5. （转）A Recipe for Training Neural Networks
- 6. STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS
- 7. Domain-Adversarial Training of Neural Networks
- 8. DANN：Domain-Adversarial Training of Neural Networks
- 9. Weighted-Entropy-based Quantization for Deep Neural Networks
- 10. Strategies For Pre-Training Graph Neural Networks